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MRI-based radiomics signature and clinical factor for predicting H3K27M mutation in pediatric high-grade gliomas located in the midline of the brain

  • Oncology
  • Published:
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Abstract

Objective

To develop a nomogram based on MRI radiomics and clinical features for preoperatively predicting H3K27M mutation in pediatric high-grade gliomas (pHGGs) with a midline location of the brain.

Methods

The institutional database was reviewed to identify patients with pHGGs with a midline location of the brain who underwent tumor biopsy with preoperative MRI scans between June 2016 and June 2021. A total of 107 patients with pHGGs, including 79 patients with H3K27M mutation, were consecutively included and randomly divided into training and test sets. Radiomics features were extracted from fluid-attenuated inversion recovery (FLAIR), diffusion-weighted (DW) and post-contrast T1-weighted images, and apparent diffusion coefficient (ADC) maps. The minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were performed for radiomics signature construction. Clinical and radiological features were analyzed to select clinical predictors. A nomogram was then developed by incorporating the radiomics signature and selected clinical predictors.

Results

Nine radiomics features were selected to construct the radiomics signature, which showed a favorable discriminatory ability in training and test sets with an area under the curve (AUC) of 0.95 and 0.92, respectively. Ring enhancement was identified as an independent clinical predictor (p < 0.01). The nomogram, constructed with radiomics signature and ring enhancement, showed good calibration and discrimination in training and testing sets (AUC: 0.95 and 0.90 respectively).

Conclusions

The nomogram which combined radiomics signature and ring enhancement had a satisfactory ability to predict H3K27M mutation in pHGGs with a midline of the brain.

Key Points

• Conventional MRI features were not powerful enough to predict H3K27M mutation status in pediatric high-grade gliomas (pHGGs) with a midline location of the brain.

• An MRI-based radiomics signature showed satisfactory ability to predict H3K27M mutation status of pHGGs located in the midline of the brain.

• Associating the radiomics signature with clinical factors improved predictive performance.

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Abbreviations

ADC:

Apparent diffusion coefficient

AIC:

Akaike’s Information Criterion

ANT:

Advanced Normalization Tools

AUC:

Area under the curve

CI:

Confidence interval

cT1W:

Post-contrast T1-weighted

DMG:

Diffuse midline glioma

DW:

Diffusion-weighted

DWI:

Diffusion-weighted images

FLAIR:

Fluid-attenuated inversion recovery

KPS:

Karnofsky performance status

LASSO:

Least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

MRMR:

Maximum relevance minimum redundancy

pHGGs:

Pediatric high-grade gliomas

Radscore:

Radiomic score

ROC:

Receiver operator characteristic

ROI:

Region of interest

SMOTE:

Synthetic minority oversampling technique

T1W:

T1-weighted

WHO:

World Health Organization

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Funding

This study was supported by grants from the National Key Research and Development Program of China (No. 2017YFC0109003) and the Projects of Science and Technology Innovation of Shanghai (No. 18411952300 and No. 18411967500).

Author information

Authors and Affiliations

  1. Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, 1665 Kongjiang Road, Shanghai, 200092, China

    Chenqing Wu, Hui Zheng, Jinning Li, Yuzhen Zhang, Yuhua Li & Dengbin Wang

  2. GE Healthcare, Pudong New Town, No.1 Huatuo Road, Shanghai, 210000, China

    Shaofeng Duan

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  1. Chenqing Wu
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  2. Hui Zheng
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Corresponding author

Correspondence to Dengbin Wang.

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Guarantor

The scientific guarantor of this publication is Dengbin Wang, MD, PhD, the chief of Department of Radiology, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Shaofeng Duan kindly provided statistical advice for this manuscript.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• retrospective

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• performed at one institution

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Chenqing Wu and Hui Zheng contributed equally to this work.

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Wu, C., Zheng, H., Li, J. et al. MRI-based radiomics signature and clinical factor for predicting H3K27M mutation in pediatric high-grade gliomas located in the midline of the brain. Eur Radiol 32, 1813–1822 (2022). https://doi.org/10.1007/s00330-021-08234-9

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